@InProceedings{passon-EtAl:2018:W18-52,
  author    = {Passon, Marco  and  Lippi, Marco  and  Serra, Giuseppe  and  Tasso, Carlo},
  title     = {Predicting the Usefulness of Amazon Reviews Using Off-The-Shelf Argumentation Mining},
  booktitle = {Proceedings of the 5th Workshop on Argument Mining},
  month     = {November},
  year      = {2018},
  address   = {Brussels, Belgium},
  publisher = {Association for Computational Linguistics},
  pages     = {35--39},
  abstract  = {Internet users generate content at unprecedented rates. Building intelligent systems capable of discriminating useful content within this ocean of information is thus becoming a urgent need. In this paper, we aim to predict the usefulness of Amazon reviews, and to do this we exploit features coming from an off-the-shelf argumentation mining system. We argue that the usefulness of a review, in fact, is strictly related to its argumentative content, whereas the use of an already trained system avoids the costly need of relabeling a novel dataset. Results obtained on a large publicly available corpus support this hypothesis.},
  url       = {http://www.aclweb.org/anthology/W18-5205}
}